Sensors or instrumentation deployed in real-world settings for various research fields, e.g. analytical chemistry, and using various detection modalities, e.g. gas chromatography, mass spectrometry, radiation detection, etc., usually produce signals corrupted by various types of noise. As a result, noise removal is a fundamental prerequisite for accurate data extraction, analysis, and interpretation, as well as effective storage/transmission. For example, noisy data would make data compression much harder and thus affect the issues of storage and transmission. If not done well, the preprocessing inherent in any instrument design can eliminate valuable information, and subsequent use of advanced signal processing methods, no matter how capable, will not be able to recover signal lost by crude pre-processing.
The techniques of wavelet transform and artificial neural network processing are each separately known in the prior art for cleaning up corrupted signals or to extract relevant information, and several notable publications are listed in the References Cited section. For example, respective publications by Voisin and Hernandez-Borges reported processing the spectra by neural networks to identify bacteria based on certain identifiers such as the concentration of n-alkanes or fatty acids as measured by gas chromatography. Additionally, respective publications by Fatemi and Jalali-Heravi reported using artificial neural networks to learn from certain chemical parameters such as molecular weights and energy levels of the highest occupied molecular orbitals, to predict retention indices or retention time. And respective publications by Bell and Cai reported using the spectra as inputs to the neural network for chemical classification such as level of toxicity or active substructures. Two examples using both wavelet transforms and artificial neural networks on chromatography, are disclosed in the respective publications by Collantes and Schirm, also listed in the References Cited section. Collantes reported using wavelets and neural networks on HPLC data for the classification of L-tryptophan from six different manufacturers. In particular a wavelet package (a combination of wavelets and an oscillating function) was used and not pure wavelets. And a relatively straightforward backpropagation neural network is used for the purpose of classification. Schirm reported using a combination of wavelet processing and neural network for quality assurance of pentosan polysulfate based on fingerprint electropherograms. Cofflet wavelets were used to preprocess the electrophoresis data. A combination of mid-level transforms was used to yield the best results for baseline and noise considerations. And a simple backpropagation neural network was used with wavelet processed data as input, for the purpose of classification and not trace signal extraction, i.e. extracting trace peaks.
Another example of a signal processing system and method using both wavelets and neural network processing is attributed to Applicants' research performed for the Lawrence Livermore National Laboratory, as disclosed in U.S. Pat. No. 6,763,339. In that patent, signal denoising is performed using wavelet processing which incorporates automatic thresholding methods followed by using a single neural network for shape-matching to extract all relevant patterns. In that technique, the neural network processing is performed in the time domain, and a single neural network is used to extract all the patterns and therefore must be pre-trained to recognize all relevant patterns.
What is still needed therefore is an efficient and more effective signal denoising and extraction technique using a combination wavelet-neural network signal processing generally applicable to a variety of research fields and modes of detection. In this manner, such a technique would enable the recovery of valuable information which may be otherwise lost to high levels of noise